---
title: "Dashboard_HW4"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    source: embed
---

```{r setup, include=FALSE}
library(flexdashboard)

library(tidyverse)
library(p8105.datasets)
library(plotly)
library(rnoaa)
library(viridis)
```

Column {data-width=650}
-----------------------------------------------------------------------

### Chart A

```{r}
dated_ny_noaa = 
  ny_noaa %>% 
  mutate(
    year = year(date),
    month = month(date),
    day = day(date)
  ) %>% 
  select(-date)

ranked_prcp = 
  dated_ny_noaa %>% 
  group_by(id) %>% 
  summarise(count_prcp = sum(prcp > 50, na.rm = TRUE)) %>% 
  ungroup() %>%
  arrange(desc(count_prcp)) %>%  
  sample_n(50) %>%  
  mutate(id = fct_reorder(id, count_prcp))

plot_ly(data = ranked_prcp, 
        x = ~id, 
        y = ~count_prcp, 
        color = ~id, 
        type = "bar", 
        colors = "viridis") %>%
  layout(title = "Random Sample of 50 IDs by Count of Days with Precipitation > 50 from 1981 to 2010",
         xaxis = list(title = "Station ID", tickangle = -90),
         yaxis = list(title = "Count of Days with Precipitation > 50"))
```


Column {data-width=350}
-----------------------------------------------------------------------

### Chart B

```{r}
avg_temp = 
  dated_ny_noaa %>% 
  mutate(
    tmax = as.numeric(tmax),
    tmin = as.numeric(tmin),
    tmax = tmax / 10,
    tmin = tmin / 10
  ) %>%
  filter(id != "USC00305925",
         id != "USC00300505",
         id != "USC00306062") %>% 
  group_by(id) %>% 
  summarise(
    avg_tmax = mean(tmax, na.rm = TRUE),
    avg_tmin = mean(tmin, na.rm = TRUE)
  )

plot_ly(
  data = avg_temp, 
  x = ~avg_tmin, 
  y = ~avg_tmax, 
  type = 'scatter', 
  mode = 'markers', 
  color = ~as.factor(id), 
  colors = viridis(n = n_distinct(avg_temp$id)),
  text = ~paste("Station ID:", id),
  marker = list(size = 10, opacity = 0.6)
) %>%
  layout(
    title = "Average Tmax vs Average Tmin by Station",
    xaxis = list(title = "Average Minimum Temperature (°C)"),
    yaxis = list(title = "Average Maximum Temperature (°C)"),
    legend = list(title = list(text = "Station ID"))
  )
```

### Chart C

```{r}
filtered_20ID = 
  dated_ny_noaa %>%
  distinct(id) %>%          
  slice(1:20) 

filtered_20ID_data = 
  dated_ny_noaa %>%
  filter(id %in% filtered_20ID$id) %>% 
  filter(prcp > 0 & prcp < 500)

plot_ly(
  data = filtered_20ID_data, 
  x = ~as.factor(id), 
  y = ~prcp, 
  type = 'box', 
  color = ~as.factor(id)
) %>%
  layout(
    title = "Boxplot of Precipitation by Station ID (First 20 Unique IDs)",
    xaxis = list(title = "Station ID", tickangle = -90),
    yaxis = list(title = "Precipitation"),
    legend = list(title = list(text = "Station ID"))
  )
```